Prioritized data object processing under processing time constraints

ABSTRACT

Systems and methods are configured to perform prioritized processing of a plurality of processing objects under a time constraint. In various embodiments, a priority policy that includes deterministic prioritization rules, probabilistic prioritization rules, and a priority determination machine learning model is applied to the objects to determine high and low priority subsets. Here, the subsets are determined using the deterministic prioritization rules and a probabilistic ordering of the low priority subset is determined using the probabilistic prioritization rules and the priority determination machine learning model. In particular embodiments, the ordering is accomplished by determining a hybrid priority score for each object in the low priority subset based on a rule-based priority score and a machine-learning-based priority score. An investigatory subset is then composed of the high priority subset and objects from the low priority subset added until a termination time according to a data processing model and the probabilistic ordering.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/900,078 filed Jun. 12, 2020, the entirety of which is incorporated byreference herein.

TECHNOLOGICAL FIELD

Embodiments of the present invention generally relate to automatedsystems and methods for investigating a listing of processing dataobjects.

BACKGROUND

Many industries are tasked with inspecting or auditing an inventory ofitems within a short timeframe. For instance, in many manufacturingenvironments, components used in manufacturing a product may need to beinspected prior to being used in production to ensure they, are notdefective. In some instances, every single component may need to beinspected before use. For example, microchips used in manufacturingsmart phones may be considered to be a critical component and therefore,smart phone manufacturers may require every microchip to be inspectedprior to being installed in a smart phone to ensure it is not defective.However, in many instances, the inspection of such components needs tobe completed in a timely fashion so as not to hold up the manufacturingprocess.

In other industries, records used in running a business may need to beaudited to ensure they are accurate. For example, for many insurancecompanies, insurance claims may need to be audited to identify thosesubject to overpayments. Here, the insurance company may wish to audit ahigh volume of claims because of the potential cost savings ofidentifying such claims. However, such audits may need to be done withina limited timeframe. For instance, the limited timeframe may be due totime constraint requirements to pay out on such claims so that theinsurance company does not face potential fines.

However, many of the conventional processes for conducting suchinvestigations operate on a first-in, first-out (FIFO) basis that is notconducive to optimizing the processes. For example, investigating itemsfound in a certain inventory on a FIFO basis can lead to theinvestigation of many items that are not defective and therefore, notnecessarily in need of investigation. In addition, these conventionalprocesses are often times carried out under some type ofconstrained/limited capacity/resource. For example, many of theseconventional processes are carried out manually and therefore personnelwho are conducting the investigations can only investigate so many itemswithin the time constraints placed on the processes. Thus, requiringpersonnel to work through items that do not necessarily need to beinvestigated can lead to a great deal of inefficient use of a limitedresource. Although some conventional processes may make use of some formof high-level filtering to attempt to pare down the number of items thatneed to be investigated, these processes still often lead to high falsepositive rates and inefficiency. In addition, because time constraintsare imposed on many conventional investigation processes, a thoroughinvestigation on a particular item often times cannot be carried out.

Therefore, a need exists in the industry for improved systems andmethods for conducting investigations of a group of items (e.g.,inventory of items) conducted under a time constraint. Morespecifically, a need exists in the industry for better utilization ofautomation to improve accuracy and efficiency in conductinginvestigations of a group of items under a time constraint. It is withrespect to these considerations and others that the disclosure herein ispresented.

BRIEF SUMMARY

In general, embodiments of the present invention provide methods,apparatus, systems, computing devices, computing entities, and/or thelike for performing prioritized processing of a plurality of processingobjects under a processing time constraint. In various embodiments, apriority policy is applied to the plurality of processing objects todetermine a high priority subset of the plurality of processing objectsand a low priority subset of the plurality of processing objects.Generally speaking, a processing object is an electronic representationof an item on which an analysis is performed to identify whether theitem is a member of a target group. For example, the processing objectsmay be electronic records of insurance claims in which an analysis isbeing performed to determine whether the claims are subject tooverpayments.

Depending on the embodiment, the priority policy may include one or moredeterministic prioritization rules, one or more probabilisticprioritization rules, and a priority determination machine learningmodel. In various embodiments, the deterministic prioritization rulesand the probabilistic prioritization rules may be identified from aplurality of candidate prioritization rules. Here, in particularembodiments, a past investigatory success measure is determined for eachof the candidate prioritization rules. Accordingly, the deterministicprioritization rules are identified as those candidate prioritizationrules whose past investigatory success measures exceed a certaintythreshold. While the probabilistic prioritization rules are identifiedas those candidate prioritization rules whose past investigatory successmeasures fall between the certainty threshold and a relevance threshold.

Accordingly, the high priority and low priority subsets may bedetermined using the one or more deterministic prioritization rules. Forinstance, in particular embodiments, the high priority subset includeseach of the processing objects that satisfies at least one of thedeterministic priority rules and the low priority subset comprises eachof the processing objects that fails to satisfy any of the deterministicprioritization rules.

In addition, a probabilistic ordering of the low priority subset may bedetermined using the probabilistic prioritization rules and the prioritydetermination machine learning model. Here, in particular embodiments,each of the probabilistic prioritization rules may be associated with aprobabilistic weight value based on a past investigatory success measurefor the corresponding rule. Accordingly, a hybrid priority score may bedetermined for each processing object in the low priority subset using arule-based priority score for the processing object based on theprobabilistic weight value for each probabilistic prioritization rulethat is satisfied by the processing object and a machine-learning-basedpriority score determined for the processing object by using thepriority determination machine learning model. In particular instances,the hybrid priority score may also be determined based on a valuationmagnitude for the processing object.

An investigatory subset may then be composed from the high priority andlow priority subsets. Accordingly, the investigatory subset may includeeach of the processing objects in the high priority subset. In addition,a data processing model may be used to add one or more of the processingobjects found in the low priority subset to the investigatory subset inaccordance with the probabilistic ordering of the subset until atermination time.

An investigatory output user interface may then be displayed in variousembodiments that describes each processing object found in theinvestigatory subset. A secondary review of each of the processingobjects found in the investigatory subset may then be conducted todetermine whether to modify an adjustment subset to include theprocessing object. The processing objects found in the adjustment subsetmay then be adjusted to generate an examination data object describingthe processing object that can be displayed on an examination userinterface.

By enabling prioritized processing of data objects under processing timeconstraints, various embodiments of the present invention reduce thenumber of processing objects that need to be processed by data objectprocessing systems during each processing iteration. This in turnreduces the computational load of data object processing systems whilemarginally affecting the effective throughput of these systems.Accordingly, by enabling prioritized processing of data objects underprocessing time constraints, various embodiments of the presentinvention enhance the efficiency and speed of various object processingsystems and make important contributions to the various computationaltasks that utilize real-time/expediated data processing.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

Having thus described the invention in general terms, reference will nowbe made to the accompanying drawings, which are not necessarily drawn toscale, and wherein:

FIG. 1 is an overview process flow in accordance with variousembodiments of the present invention;

FIG. 2 is a diagram of a system architecture that can be used inconjunction with various embodiments of the present invention;

FIG. 3 is a schematic of a computing entity in accordance with variousembodiments of the present invention;

FIG. 4 is a process flow for sorting an inventory of processing objectsin accordance with various embodiments of the present invention;

FIG. 5 is a process flow for prioritizing a set of low priorityprocessing objects in accordance with various embodiments of the presentinvention;

FIG. 6 is a process flow for prioritizing a set of high priorityprocessing objects in accordance with various embodiments of the presentinvention;

FIG. 7 is a process flow for investigating one or more prioritized listsof processing objects in accordance with various embodiments of thepresent invention;

FIG. 8 is a process flow for investigating a particular processingobject in accordance with various embodiments of the present invention.

DETAILED DESCRIPTION OF SOME EXAMPLE EMBODIMENTS

Various embodiments of the present invention now will be described morefully hereinafter with reference to the accompanying drawings, in whichsome, but not all embodiments of the inventions are shown. Indeed, theseinventions may be embodied in many different forms and should not beconstrued as limited to the embodiments set forth herein; rather, theseembodiments are provided so that this disclosure will satisfy applicablelegal requirements. The term “or” (also designated as “/”) is usedherein in both the alternative and conjunctive sense, unless otherwiseindicated. The terms “illustrative” and “exemplary” are used to beexamples with no indication of quality level. Like numbers refer to likeelements throughout.

Definitions of Certain Terms

The term “processing object” may refer to a data object that describesan item on which an analysis is performed in various embodiments toidentify whether the item is a member of a target group. For instance,in particular embodiments, the item may be an insurance claim and theprocessing object may be an electronic record of the claim used inconducting an analysis to determine whether the claim may be subject tooverpayment and need to be reviewed. While in other embodiments, theitem may be a manufactured component and the processing object may be anelectronic entry representing the component used in conducting ananalysis to determine whether the component may have a defect and needto be inspected prior to shipment. Here, the processing object may beassociated with a set of information that may be used in conducting theanalysis. For example, the processing object for an insurance claim mayinclude information on the medical procedure for which the claim isbeing submitted as well as information on the party submitting theclaim. While the processing object for a manufactured component mayinclude information recorded on the manufacturing conditions that werepresent when the component was assembled and/or tested properties of thecomponent.

The term “processing time constraint” may refer to a data object thatdescribes one or more limits placed on the amount of time allocated tohandle a group of processing objects. For instance, a processing timeconstraint may be defined as a set timeframe (e.g., twenty-four hours)allowed for analyzing the group of processing objects to identify whichof the objects are members of a target group, and if so, handle suchobject in an appropriate manner.

The term “deterministic prioritization rules” may refer to a data objectthat describes a set of rules used for separating a group of processingobjects into high priority objects and low priority objects. Forexample, deterministic prioritization rules may represent variousmanufacturing rules used in sorting manufactured components into highpriority objects that may have defects and low priority objects forinspection purposes.

The term “probabilistic prioritization rules” may refer to a data objectthat describes a set of rules used for arranging a group of processingobjects in order of importance relative to each other with respect to alikelihood of being a member of a target group. For example,probabilistic prioritization rules may be used to prioritize a number ofinsurance claims for review that are subject to a time constraint sothat the more important claims (e.g., claims that are more likely to besubject to overpayments) are reviewed first.

The term “priority determination machine learning model” may refer to adata object that describes parameters and/or hyper-parameters of one ormore predictive models used in various embodiments for providing aprobability for a processing object with respect to the object being amember of a target group. For example, in an insurance claimsenvironment, the priority determination machine learning model mayprovide a probability that an insurance claim is subject to anoverpayment.

The term “priority policy” may refer to a data object that describes adefined process for handling a group of processing objects with respectto prioritizing the processing of the objects. For instance, inparticular embodiments, a priority policy may be made up of acombination of one or more deterministic prioritization rules, one ormore probabilistic prioritization rules, and one or more prioritydetermination machine learning models that are utilized in prioritizingobjects to provide an order of importance for processing.

The term “investigatory output user interface” may refer to a dataobject that describes a user interface that can be used by individualsfor viewing processing objects identified for investigation. Forexample, the investigatory output user interface may be used byindividuals in particular embodiments to view processing objects forinsurance claims that have been identified as subject to possibleoverpayments so that the claims can be investigated as to whether theyare actually subject to overpayments.

The term “past investigatory success measure” may refer to a data objectthat describes a measure of an accuracy of a particular rule againsthistorical cases with known outcomes. For instance, in particularembodiments, a past investigatory success measure may be a measure ofhow accurately a corresponding rule for the measure identifiesprocessing objects are members of a target group.

The term “probabilistic weight value” may refer to a data object thatdescribes a weighting that applies to a probabilistic prioritizationrule. Here, in particular embodiments, the weighting may be determinedbased on a past investigatory success measure for the correspondingprobabilistic prioritization rule.

The term “rule-based priority score” may refer to a data object thatdescribes a value determined for a processing object based on theprobabilistic weight values of one or more probabilistic prioritizationrules that are satisfied by the processing object.

The term “machine-learning-based priority score” may refer to a dataobject that describes a value determined for a processing object usingthe priority determination machine learning model. For instance, inparticular embodiments, this score represents a probability of theprocessing object being a member of a target group.

The term “valuation magnitude” may refer to a data object that describesa measure of the utility/economic value of a processing object. Forexample, the valuation magnitude may be a monetary value of theprocessing object. While in another example, the valuation magnitude maybe a perceived importance of the processing object. For instance, theimportance of a manufactured component used in assembling a largercomponent such as, the importance of defect-free brake rotors used inautomobile production.

The term “certainty threshold” may refer to a data object that describesa threshold magnitude used for determining whether a candidateprioritization rule is defined as a deterministic prioritization rule.For instance, in particular embodiments, a candidate prioritization rulewhom past investigatory success measure exceeds the certainty thresholdmay be defined as a deterministic prioritization rule. Here, thecertainty threshold is used as a measure for determining whether acandidate prioritization rule should be defined as a deterministicprioritization rule based on how well the candidate prioritization ruleaccurately identifies processing objects are members of a target group.

The term “relevance threshold” may refer to a data object that describesa threshold magnitude used for determining whether a candidateprioritization rule is defined as a probabilistic prioritization rule.For instance, in particular embodiments, a candidate prioritization rulewhom past investigatory success measure falls between the certaintythreshold and the relevance threshold may be defined as a probabilisticprioritization rule. Similar to the certainty threshold, the relevancethreshold is used as a measure for determining whether a candidateprioritization rule should be defined as a probabilistic prioritizationrule based on how well the candidate prioritization rule accuratelyidentifies processing objects are members of a target group. Althoughthe candidate prioritization rule may not have a level of accuracyacceptable for being defined as a deterministic prioritization rule, thecandidate prioritization rule may still have a level of accuracyacceptable for being defined as a probabilistic prioritization rule.

Overview of Various Embodiments of the Invention

An overview is now provided to demonstrate various embodiments of theinvention. With that said, an example is now described that is usedthroughout the disclosure to demonstrate various embodiments of theinvention. This example is provided to assist the reader inunderstanding these embodiments and should not be construed to limit thescope of the invention.

The example involves auditing an inventory of medical insurance claimswith respect to payment integrity prior to pre-paying on the claims toensure the claims are not subject to overpayments. The term “auditing”is used here to indicate that some type of analysis will be performed toidentify those claims found in the inventory that are subject tooverpayments. Overpayment on such claims can be a considerable cost toinsurance companies and identifying claims subject to overpayments candrive significant annual savings for these companies. The conventionalprocess for auditing these claims are the claims are investigated on aFIFO basis that can be susceptible to a number of limitations. First,the claims are often required to be investigated under a time constraintsuch as, for example, thirty-six hours. Second, the investigation istypically carried out using a limited resource. For example, manyconventional processes used in investigating insurance claims involvemanual intervention. Therefore, a limited resource becomes the auditorsinvestigating the claims and their physical limitations of only beingable to conduct investigations of so many claims under the timeconstraint. This can lead to auditors' inability to complete thoroughinvestigation of these claims. Accordingly, various embodiments of theinvention allow for optimization of how these claims are worked,resulting in reduced false positive rates, shortened turnaround times,and increased efficiencies and savings.

Although the example of auditing the health insurance claims to identifyclaims subject to overpayments is used throughout the disclosure todemonstrate various embodiments of the invention, those of ordinaryskill in the art should recognize that the invention is also applicableto other environments involving the analyzing (e.g., the auditing orinspecting) of a plurality of items to identify those items that aremembers of a target group. For example, various embodiments of theinvention may be used in a manufacturing environment to identify thosecomponents used in manufacturing a product that are defective from aninventory. Here, the components may be represented by processing objectsthat serve as electronic representations of the components that can beprocessed to identify which of the components in the inventory arelikely to be defective. These components can then be sorted out forfurther inspection either manually or through automation.

For example, the steel industry many times uses some type of automatedcamera system for inspecting steel coils to identify coils with surfacedefects. In this example, various embodiments of the invention can beused in identifying which of the coils are likely to have surfacedefects based on analyzing processing objects providing information(values) on different variables experienced during the manufacturing ofthe coils such as, for example, rolling loads and temperatures that werepresent when the coils where manufactured from steel slabs. Here,various embodiments of the invention can allow for the identification ofwhich coils are likely to have surface defects from the processingobjects for the coils without the steel company having to actuallyinspect every single coil using the automated camera system. Those ofordinary skill in the art can envision other environments that variousembodiments of the invention may be used in light of this disclosure.

Brief Overview of Technical Problem

Typically, investigative processes used in many industries are taskedwith investigating (e.g., inspecting and/or auditing) an inventory ofitems daily that must be carried out within a short timeframe. Here, theterm “inventory” generally refers to a plurality of items. Suchinvestigative processes are often carried out to identify which itemsfound in the inventory fall into a target group. For example, toidentify which components found in an inventory of components for amanufacturing process are defective or which data records found in aregistry used in an industry setting have inaccurate informationrecorded for the records. Identification of such items can lead toimproved quality, higher efficiencies, and cost savings. However,current approaches are often manually driven and typically require humanintervention to implement these investigative processes. In addition tobeing dependent on manual processes, existing solutions are also oftendependent on the availability of some type of constrained capacityand/or limited resource such as personnel and/or inspection equipmentand are subject to be completed under some type of time constraint.

Brief Overview of Technical Solution

Embodiments of the present invention provide concepts for replacing theexisting manually driven processes of investigating (e.g., inspecting orauditing) items to identify those that fall into a target group with anautomated process. In various embodiments, one or more deterministicprioritization rules are automatically applied to each of the itemsfound in an inventory to sort the items into a set of items having ahigh priority for being investigated and a low priority for beinginvestigated. The low priority items are then processed using one ormore probabilistic prioritization rules and a weighting is assigned toeach item according to the rules that apply to that item. In addition,one or more machine learning models are applied to each of the lowpriority items to identify a probability of the item being in the targetgroup. A novel hybrid probability score is then determined for each ofthe low priority items based on both the weighting assigned to the itemand the probability determined using the machine learning model(s). Thishybrid probability score is used to prioritize the low priority itemswith respect to each other to provide an order of importance forinvestigation. The result is a listing of the items found in theinventory that provides a higher priority to items with a higherlikelihood of being a member of the target group. As a result, thehigher prioritized items are investigated earlier in the process, whichis beneficial when time constraints are in place. In addition, many ofthe investigating processes may be automated to use the listing ofprioritized items.

The disclosed solution is more effective, accurate, and faster thanhuman investigation. Further, the deterministic prioritization rules,probabilistic prioritization rules, and machine learning model(s) cancarry out complex mathematical operations that cannot be performed bythe human mind (e.g., determining a likelihood of items being a memberof a target group). Especially when such operations need to be performedin a short timeframe. Additionally, the solution eliminates the need toinspect/audit items that actually do not need inspection, makes moreefficient use of constrained capacities and limited resources, andallows for more thorough investigation of items in a timely fashion.

Brief Overview of Various Embodiments

Turning now to FIG. 1, an overview process flow 100 is shown inaccordance with various embodiments of the invention. The process flow100 is discussed with respect to the example involving investigating aninventory of health insurance claims to identify those claims subject tooverpayments. Again, the term “inventory” is used to simply refer to aplurality of health insurance claims. Here, the processing objects usedin the investigation are electronic records representing the claims.Each of the records include various information on a claim such as, forexample, the party submitting the claim, specifics on the party'sinsurance plan and coverage, the monetary value of the claim, thehealthcare provider submitting the claim on behalf of the party, etc.Initially, each of the claims may be reviewed by a claims examiner 110who determines the claim may be suspect and should be audited forpotential overpayment. Therefore, the claims examiner 110 may place theclaim into a specific inventory 115 for analysis.

Various embodiments of the invention are configured to prioritize theinventory of claims 115 based on the likelihood of overpayment using acombination of rules and one or more machine learning models. Theprioritized inventory 150 is then processed using automation to performan investigation on the inventory. Accordingly, any claim identified assuspect during the investigation is sent to an auditor 165 for review.Otherwise, review of the claim is completed, and the claim isautomatically routed back to the originating claims examiner 110,bypassing the audit team for review. Here, the process flow 100 is beingconducted under a processing time constraint that requires the inventory115 to be investigated within a limited timeframe.

Therefore, in various embodiments, the claims placed in the inventory115 are initially investigated via an automated process using a set ofdeterministic prioritization rules 120. Depending on the embodiments,these deterministic prioritization rules 120 may be identified usingdifferent criteria. However, in general, each of the deterministicprioritization rules 120 is ideally sufficient at identifying aninsurance claim that is likely subject to an overpayment. For example, adeterministic prioritization rule 120 may be defined that if the claimis for a particular medical procedure to be performed by a particularhealthcare provider, then the rule 120 applies to the claim. The reasonfor this particular deterministic prioritization rule may be that thehealthcare provider has a past history of submitting claims foroverpayment on the particular medical procedure.

In particular embodiments, the set of deterministic prioritization rules120 may be developed from a set of candidate prioritization rules inwhich the deterministic prioritization rules 120 are identify as thoserules with a past investigatory success measure over a certaintythreshold. In general, the past investigatory success measure is ameasure of an accuracy of a particular rule against historical caseswith known outcomes. Therefore, in this example, the past investigatorysuccess measure provides a measure of how accurately the correspondingcandidate prioritization rule for the measure identifies an insuranceclaim that is subject to an overpayment.

Accordingly, the deterministic prioritization rules 120 are applied toeach of the processing objects in the inventory 115 to sort theinventory 115 into a high priority list of claims 125 and a low prioritylist of claims 130. In particular embodiments, the claims found in thehigh priority list 125 are those claims identified as having a highlikelihood to being subject to overpayments and therefore are givenpriority over the claims found in the low priority list 130. This helpsto ensure the high priority claims are investigated for overpaymentswithin the processing time constraint. In some instances, the claimsfound in the high priority list 125 may be prioritized relative eachother so that the claims with higher importance (e.g., the claims ofhigh monetary value) are processed first.

Next, the claims found in the low priority list 130 are prioritized toprovide an order of importance relative to each other. Here, inparticular embodiments, a set of probabilistic prioritization rules 135are applied to each claim to identify those rules in the set ofprobabilistic prioritization rules 135 that are applicable to the claim.

Similar to the deterministic prioritization rules 120, the probabilisticprioritization rules 135 may be identified using different criteria. Inaddition, the set of probabilistic prioritization rules 135 may be basedon a set of candidate prioritization rules in which the probabilisticprioritization rules 135 are identified as those rules with a pastinvestigatory success measure over a relevance threshold. The relevancethreshold is typically less than the certainty threshold used inidentifying the deterministic prioritization rules 120. Therefore,candidate rules that do not necessarily have a past investigatorysuccess measure that is high enough to be identified as a deterministicprioritization rule 120 may still be identified as a probabilisticprioritization rule 135. Again, the basis of the set of probabilisticprioritization rules 135 in various embodiments is a set of rules thathave a certain level of success in identifying insurance claims subjectto overpayments.

In various embodiments, a probabilistic weight value is also assigned toeach of the probabilistic prioritization rules 135 that is based on therule's past investigatory success measure. Accordingly, a rule-basedpriority score may be determined for each claim found in the lowpriority list 130 based on the probabilistic weight values of theprobability prioritization rules 135 that are applicable to the claim.For example, the probabilistic weight values for the applicable rules135 may be combined (e.g., added or multiplied together) to determinethe rule-based priority score for a claim. In addition, one or moremachine learning models 140 are used to produce a score for each claimfound in the low priority list 130 identifying a probability of theclaim being subject to an overpayment. As described in further detailherein, the one or more machine learning models 140 may be any number ofdifferent types of predictive models.

At this point, in particular embodiments, a hybrid prioritization score145 is determined for each low priority claim by combining the claim'srule-based priority score and machine-learning-based probability score.Depending on the embodiment, a number of different formulas may be usedin combining the two scores. For instance, in particular embodiments,the hybrid prioritization score 145 for each claim is determined bymultiplying the rule-based priority score, the machine-learning-basedprobability score, and a valuation magnitude for the claim. The valuemagnitude provides a measure of the value of the claim. For example, thevalue magnitude may be the dollar amount of the payment on the claim.

The hybrid prioritization score is then used in various embodiments toconstruct a prioritized inventory 150 for the low priority claims. Thisprioritized inventory 150 provides a listing of the low priority claimsin order of importance relative to each other. Here, the importance isassociated with the likelihood the low priority claim is subject to anoverpayment relative to the other low priority claims found in theprioritized inventory 150 and therefore, the importance of beinginvestigated within the processing time constraint placed on theinvestigation process.

A number of automated checks 155 are then performed in variousembodiments on the claims to identify those claims with issues thatsuggest the claims are likely subject to overpayments. In particularembodiments, these automated checks 155 may be in the form of one ormore rules that are applied by some type of automation such as roboticprocess automation. Depending on the embodiment, the process flow 100may be designed to have the automated checks 155 performed on both thehigh priority list of claims 125 and the low priority list of claims 130as prioritized in the prioritized inventory 150 or just on the lowpriority list of claims 130. If both the high priority list of claims125 and the low priority list of claims 130 are to have the automatedchecks 155 performed on them, then the high priority list of claims 130are typically processed first before the prioritized inventory 150 forthe low priority list of claims 130. This ensures the high priorityclaims are processed within the processing time constraint. However, inother instances, the high priority list of claims 125 may bypass theautomated checks 155 and be sent directly to the auditors 165 forreview.

Accordingly, the automated checks 155 are performed on the claims basedon their prioritization to identify those claims that fail one or moreof the checks and therefore, should be reviewed by the auditors 165.Therefore, the claims are selected from the prioritized inventory 150based on their priority relative to one another. In particularembodiments, any claims not processed by the automated checks 155 withinthe processing time constraint are deprioritized and sent back to theclaims examiners 110. Here, the deprioritized claims are considered inmany instances to have lower probabilities of overpayment and/or lowervalues (e.g., dollar amounts).

For those claims processed by the automated checks 155, a determination160 is made for each claim as to whether the claim passes the checks. Afull review of the claim is completed and if no issues are found, thenthe claim is routed back to the originating claim examiner 110 asaudited inventory 185 without any human intervention. That is to say,the claim is returned to the audited inventory 185, passing the need forauditor review.

However, if the automated checks 155 have identified a potential erroron the claim, then the claim is sent to an auditor 165 forinvestigation. Here, the claim has only been partially reviewed and nowrequires an auditor 165 to complete the review before sending it back tothe originating claims examiner 110. Here, the auditor 165 may conductinvestigations of the claims that have failed one or more checks basedon their prioritization 170. For example, in particular embodiments, theprocessing objects for the different claims that have failed the checksmay be displayed on some type of user interface that allows the auditor165 to review the information for the claims and conduct investigationsby analyzing the information and/or accessing and analyzing additionalinformation on the claims as needed. If a defect in a claim if found175, then the claim may be adjusted accordingly 180. For example, thedollar amount of the claim may be adjusted or the claim may be markedfor the claim examiner 110 to decline. At this point, the originatingclaim examiner 110 processes the claim according to the outcome of theinvestigation conducted on the claim and places the claim into theaudited inventory 185.

Thus, various embodiments of the invention based on the process flow 100shown in FIG. 1 allow for health insurance claims with a high likelihoodof overpayments to be identified and worked as a priority. In addition,various embodiments allow auditors 165 more time to work on claims witha high chance of savings as compliant claims are automatically passedthrough the investigation process without any human intervention. Thisallows for increased claim volume that is likely subject to overpaymentto be worked in a more efficient and effective manner.

Computer Program Products, Systems, Methods, and Computing Entities

Embodiments of the present invention may be implemented in various ways,including as computer program products that comprise articles ofmanufacture. Such computer program products may include one or moresoftware components including, for example, software objects, methods,data structures, and/or the like. A software component may be coded inany of a variety of programming languages. An illustrative programminglanguage may be a lower-level programming language such as an assemblylanguage associated with a particular hardware architecture and/oroperating system platform. A software component comprising assemblylanguage instructions may require conversion into executable machinecode by an assembler prior to execution by the hardware architectureand/or platform. Another example programming language may be ahigher-level programming language that may be portable across multiplearchitectures. A software component comprising higher-level programminglanguage instructions may require conversion to an intermediaterepresentation by an interpreter or a compiler prior to execution.

Other examples of programming languages include, but are not limited to,a macro language, a shell or command language, a job control language, ascript language, a database query or search language, and/or a reportwriting language. In one or more example embodiments, a softwarecomponent comprising instructions in one of the foregoing examples ofprogramming languages may be executed directly by an operating system orother software component without having to be first transformed intoanother form. A software component may be stored as a file or other datastorage construct. Software components of a similar type or functionallyrelated may be stored together such as, for example, in a particulardirectory, folder, or library. Software components may be static (e.g.,pre-established or fixed) or dynamic (e.g., created or modified at thetime of execution).

A computer program product may include a non-transitorycomputer-readable storage medium storing applications, programs, programmodules, scripts, source code, program code, object code, byte code,compiled code, interpreted code, machine code, executable instructions,and/or the like (also referred to herein as executable instructions,instructions for execution, computer program products, program code,and/or similar terms used herein interchangeably). Such non-transitorycomputer-readable storage media include all computer-readable media(including volatile and non-volatile media).

In one embodiment, a non-volatile computer-readable storage medium mayinclude a floppy disk, flexible disk, hard disk, solid-state storage(SSS) (e.g., a solid state drive (SSD), solid state card (SSC), solidstate module (SSM), enterprise flash drive, magnetic tape, or any othernon-transitory magnetic medium, and/or the like. A non-volatilecomputer-readable storage medium may also include a punch card, papertape, optical mark sheet (or any other physical medium with patterns ofholes or other optically recognizable indicia), compact disc read onlymemory (CD-ROM), compact disc-rewritable (CD-RW), digital versatile disc(DVD), Blu-ray disc (BD), any other non-transitory optical medium,and/or the like. Such a non-volatile computer-readable storage mediummay also include read-only memory (ROM), programmable read-only memory(PROM), erasable programmable read-only memory (EPROM), electricallyerasable programmable read-only memory (EEPROM), flash memory (e.g.,Serial, NAND, NOR, and/or the like), multimedia memory cards (MMC),secure digital (SD) memory cards, SmartMedia cards, CompactFlash (CF)cards, Memory Sticks, and/or the like. Further, a non-volatilecomputer-readable storage medium may also include conductive-bridgingrandom access memory (CBRAM), phase-change random access memory (PRAM),ferroelectric random-access memory (FeRAM), non-volatile random-accessmemory (NVRAM), magnetoresistive random-access memory (MRAM), resistiverandom-access memory (RRAM), Silicon-Oxide-Nitride-Oxide-Silicon memory(SONOS), floating junction gate random access memory (FJG RAM),Millipede memory, racetrack memory, and/or the like.

In one embodiment, a volatile computer-readable storage medium mayinclude random access memory (RAM), dynamic random access memory (DRAM),static random access memory (SRAM), fast page mode dynamic random accessmemory (FPM DRAM), extended data-out dynamic random access memory (EDODRAM), synchronous dynamic random access memory (SDRAM), double datarate synchronous dynamic random access memory (DDR SDRAM), double datarate type two synchronous dynamic random access memory (DDR2 SDRAM),double data rate type three synchronous dynamic random access memory(DDR3 SDRAM), Rambus dynamic random access memory (RDRAM), TwinTransistor RAM (TTRAM), Thyristor RAM (T-RAM), Zero-capacitor (Z-RAM),Rambus in-line memory module (RIMM), dual in-line memory module (DIMM),single in-line memory module (SIMM), video random access memory (VRAM),cache memory (including various levels), flash memory, register memory,and/or the like. It will be appreciated that where embodiments aredescribed to use a computer-readable storage medium, other types ofcomputer-readable storage media may be substituted for or used inaddition to the computer-readable storage media described above.

As should be appreciated, various embodiments of the present inventionmay also be implemented as methods, apparatus, systems, computingdevices, computing entities, and/or the like. As such, embodiments ofthe present invention may take the form of a data structure, apparatus,system, computing device, computing entity, and/or the like executinginstructions stored on a computer-readable storage medium to performcertain steps or operations. Thus, embodiments of the present inventionmay also take the form of an entirely hardware embodiment, an entirelycomputer program product embodiment, and/or an embodiment that comprisescombination of computer program products and hardware performing certainsteps or operations.

Embodiments of the present invention are described below with referenceto block diagrams and flowchart illustrations. Thus, it should beunderstood that each block of the block diagrams and flowchartillustrations may be implemented in the form of a computer programproduct, an entirely hardware embodiment, a combination of hardware andcomputer program products, and/or apparatus, systems, computing devices,computing entities, and/or the like carrying out instructions,operations, steps, and similar words used interchangeably (e.g., theexecutable instructions, instructions for execution, program code,and/or the like) on a computer-readable storage medium for execution.For example, retrieval, loading, and execution of code may be performedsequentially such that one instruction is retrieved, loaded, andexecuted at a time. In some exemplary embodiments, retrieval, loading,and/or execution may be performed in parallel such that multipleinstructions are retrieved, loaded, and/or executed together. Thus, suchembodiments can produce specifically configured machines performing thesteps or operations specified in the block diagrams and flowchartillustrations. Accordingly, the block diagrams and flowchartillustrations support various combinations of embodiments for performingthe specified instructions, operations, or steps.

Exemplary System Architecture

FIG. 2 provides an illustration of a system architecture 200 that may beused in accordance with various embodiments of the invention. Here, thearchitecture 200 includes various components for conductinginvestigations (e.g., inspections and/or audits) on various processingobjects representing items that are to be identify as belonging or notto a target group. Accordingly, the components may include one or moreapplication servers 210 and one or more data storage 215 incommunication over one or more networks 220. It should be understoodthat the application server(s) 210 and data storage 215 may be made upof several servers, storage media, layers, and/or other components,which may be chained or otherwise configured to interact and/or performtasks. Specifically, the application server(s) 210 may include anyappropriate hardware and/or software for interacting with the datastorage 215 as needed to execute aspects of one or more applications forconducting investigations of various processing objects representingitems and handling data access and business logic for such. Furthermore,the data storage 215 may be a device or any combination of devicescapable of storing, accessing, and retrieving data. For instance,depending on the embodiment, the data storage 215 may comprise anycombination and number of data servers and data storage media in anystandard, distributed, or clustered configuration. In variousembodiments, the application server(s) 210 provide access controlservices in cooperation with the data storage 215 and are configured togenerate content that may be displayed on one or more computing devicesin an appropriate structured language such as, for example, HypertextMarkup Language (“HTML”) and/or eXtensible Markup Language (“XML”).

In addition, the architecture 200 may include one or more computingdevices 225, 230 used by individuals to further conduct investigationson various processing objects for items. For example, the computingdevices 225, 230 may be used by auditor(s) and claims examiner(s) inconducting investigations and viewing the results of investigations onclaims identified as potentially subject to overpayments. Here, thesedevices 225, 230 may be one of many different types of devices such as,for example, a desktop or laptop computer or a mobile device such as asmart phone or tablet.

As noted, the application server(s) 210, data storage 215, and computingdevices 225, 230 may communicate with one another over one or morenetworks 220. Depending on the embodiment, these networks 220 maycomprise any type of known network such as a land area network (LAN),wireless land area network (WLAN), wide area network (WAN), metropolitanarea network (MAN), wireless communication network, the Internet, etc.,or combination thereof. In addition, these networks 220 may comprise anycombination of standard communication technologies and protocols. Forexample, communications may be carried over the networks 220 by linktechnologies such as Ethernet, 802.11, CDMA, 3G, 4G, or digitalsubscriber line (DSL). Further, the networks 220 may support a pluralityof networking protocols, including the hypertext transfer protocol(HTTP), the transmission control protocol/internet protocol (TCP/IP), orthe file transfer protocol (FTP), and the data transferred over thenetworks 220 may be encrypted using technologies such as, for example,transport layer security (TLS), secure sockets layer (SSL), and internetprotocol security (IPsec). Those skilled in art will recognize FIG. 2represents but one possible configuration of a system architecture 200,and that variations are possible with respect to the protocols,facilities, components, technologies, and equipment used.

Exemplary Computing Entity

FIG. 3 provides a schematic of a computing entity 300 according tovarious embodiments of the present invention. For instance, thecomputing entity 300 may be an application server 210 and/or computingdevices 225, 230 found within the system architecture 200 previouslydescribed in FIG. 2. In general, the terms computing entity, entity,device, system, and/or similar words used herein interchangeably mayrefer to, for example, one or more computers, computing entities,desktop computers, mobile phones, tablets, phablets, notebooks, laptops,distributed systems, items/devices, terminals, servers or servernetworks, blades, gateways, switches, processing devices, processingentities, set-top boxes, relays, routers, network access points, basestations, the like, and/or any combination of devices or entitiesadapted to perform the functions, operations, and/or processes describedherein. Such functions, operations, and/or processes may include, forexample, transmitting, receiving, operating on, processing, displaying,storing, determining, creating/generating, monitoring, evaluating,comparing, and/or similar terms used herein interchangeably. In oneembodiment, these functions, operations, and/or processes can beperformed on data, content, information, and/or similar terms usedherein interchangeably.

Although illustrated as a single computing entity, those of ordinaryskill in the art should appreciate that the computing entity 300 shownin FIG. 3 may be embodied as a plurality of computing entities, tools,and/or the like operating collectively to perform one or more processes,methods, and/or steps. As just one non-limiting example, the computingentity 300 may comprise a plurality of individual data tools, each ofwhich may perform specified tasks and/or processes.

Depending on the embodiment, the computing entity 300 may include one ormore network and/or communications interfaces 325 for communicating withvarious computing entities, such as by communicating data, content,information, and/or similar terms used herein interchangeably that canbe transmitted, received, operated on, processed, displayed, stored,and/or the like. For instance, the computing entity 300 may be anapplication server 210 communicating with other computing entities suchas one or more devices 225, 230 being used by auditor(s) and/or claimexaminer(s) and/or the like. Thus, in certain embodiments, the computingentity 300 may be configured to receive data from one or more datasources and/or devices as well as receive data indicative of input, forexample, from a device.

As already mentioned, the networks used for communicating may include,but are not limited to, any one or a combination of different types ofsuitable communications networks such as, for example, cable networks,public networks (e.g., the Internet), private networks (e.g.,frame-relay networks), wireless networks, cellular networks, telephonenetworks (e.g., a public switched telephone network), or any othersuitable private and/or public networks. Further, the networks may haveany suitable communication range associated therewith and may include,for example, global networks (e.g., the Internet), MANs, WANs, LANs, orPANs. In addition, the networks may include any type of medium overwhich network traffic may be carried including, but not limited to,coaxial cable, twisted-pair wire, optical fiber, a hybrid fiber coaxial(HFC) medium, microwave terrestrial transceivers, radio frequencycommunication mediums, satellite communication mediums, or anycombination thereof, as well as a variety of network devices andcomputing platforms provided by network providers or other entities.

Accordingly, such communication may be executed using a wired datatransmission protocol, such as fiber distributed data interface (FDDI),digital subscriber line (DSL), Ethernet, asynchronous transfer mode(ATM), frame relay, data over cable service interface specification(DOCSIS), or any other wired transmission protocol. Similarly, thecomputing entity 300 may be configured to communicate via wirelessexternal communication networks using any of a variety of protocols,such as general packet radio service (GPRS), Universal MobileTelecommunications System (UMTS), Code Division Multiple Access 2000(CDMA2000), CDMA2000 1× (1×RTT), Wideband Code Division Multiple Access(WCDMA), Global System for Mobile Communications (GSM), Enhanced Datarates for GSM Evolution (EDGE), Time Division-Synchronous Code DivisionMultiple Access (TD-SCDMA), Long Term Evolution (LTE), Evolved UniversalTerrestrial Radio Access Network (E-UTRAN), Evolution-Data Optimized(EVDO), High Speed Packet Access (HSPA), High-Speed Downlink PacketAccess (HSDPA), IEEE 802.11 (Wi-Fi), Wi-Fi Direct, 802.16 (WiMAX),ultra-wideband (UWB), infrared (IR) protocols, near field communication(NFC) protocols, Wibree, Bluetooth protocols, wireless universal serialbus (USB) protocols, and/or any other wireless protocol. The computingentity 300 may use such protocols and standards to communicate usingBorder Gateway Protocol (BGP), Dynamic Host Configuration Protocol(DHCP), Domain Name System (DNS), File Transfer Protocol (FTP),Hypertext Transfer Protocol (HTTP), HTTP over TLS/SSL/Secure, InternetMessage Access Protocol (IMAP), Network Time Protocol (NTP), Simple MailTransfer Protocol (SMTP), Telnet, Transport Layer Security (TLS), SecureSockets Layer (SSL), Internet Protocol (IP), Transmission ControlProtocol (TCP), User Datagram Protocol (UDP), Datagram CongestionControl Protocol (DCCP), Stream Control Transmission Protocol (SCTP),HyperText Markup Language (HTML), and/or the like.

In addition, in various embodiments, the computing entity 300 includesor is in communication with one or more processing elements 310 (alsoreferred to as processors, processing circuitry, and/or similar termsused herein interchangeably) that communicate with other elements withinthe computing entity 300 via a bus 330, for example, or networkconnection. As will be understood, the processing element 310 may beembodied in several different ways. For example, the processing element310 may be embodied as one or more complex programmable logic devices(CPLDs), microprocessors, multi-core processors, coprocessing entities,application-specific instruction-set processors (ASIPs), and/orcontrollers. Further, the processing element 310 may be embodied as oneor more other processing devices or circuitry. The term circuitry mayrefer to an entirely hardware embodiment or a combination of hardwareand computer program products. Thus, the processing element 310 may beembodied as integrated circuits, application specific integratedcircuits (ASICs), field programmable gate arrays (FPGAs), programmablelogic arrays (PLAs), hardware accelerators, other circuitry, and/or thelike. As will therefore be understood, the processing element 310 may beconfigured for a particular use or configured to execute instructionsstored in volatile or non-volatile media or otherwise accessible to theprocessing element 310. As such, whether configured by hardware,computer program products, or a combination thereof, the processingelement 310 may be capable of performing steps or operations accordingto embodiments of the present invention when configured accordingly.

In various embodiments, the computing entity 300 may include or be incommunication with non-volatile media (also referred to as non-volatilestorage, memory, memory storage, memory circuitry and/or similar termsused herein interchangeably). For instance, the non-volatile storage ormemory may include one or more non-volatile storage or memory media 320such as hard disks, ROM, PROM, EPROM, EEPROM, flash memory, MMCs, SDmemory cards, Memory Sticks, CBRAM, PRAM, FeRAM, RRAM, SONOS, racetrackmemory, and/or the like. As will be recognized, the non-volatile storageor memory media 320 may store files, databases, database instances,database management system entities, images, data, applications,programs, program modules, scripts, source code, object code, byte code,compiled code, interpreted code, machine code, executable instructions,and/or the like. The term database, database instance, databasemanagement system entity, and/or similar terms used hereininterchangeably and in a general sense to refer to a structured orunstructured collection of information/data that is stored in acomputer-readable storage medium.

In particular embodiments, the memory media 320 may also be embodied asa data storage device or devices, as a separate database server orservers, or as a combination of data storage devices and separatedatabase servers. Further, in some embodiments, the memory media 320 maybe embodied as a distributed repository such that some of the storedinformation/data is stored centrally in a location within the system andother information/data is stored in one or more remote locations.Alternatively, in some embodiments, the distributed repository may bedistributed over a plurality of remote storage locations only. Asalready discussed, various embodiments contemplated herein data storage215 in which some or all the information/data required for conductinginvestigations on processing objects representing items may be stored.

In various embodiments, the computing entity 300 may further include orbe in communication with volatile media (also referred to as volatilestorage, memory, memory storage, memory circuitry and/or similar termsused herein interchangeably). For instance, the volatile storage ormemory may also include one or more volatile storage or memory media 315as described above, such as RAM, DRAM, SRAM, FPM DRAM, EDO DRAM, SDRAM,DDR SDRAM, DDR2 SDRAM, DDR3 SDRAM, RDRAM, RIMM, DIMM, SIMM, VRAM, cachememory, register memory, and/or the like. As will be recognized, thevolatile storage or memory media 315 may be used to store at leastportions of the databases, database instances, database managementsystem entities, data, images, applications, programs, program modules,scripts, source code, object code, byte code, compiled code, interpretedcode, machine code, executable instructions, and/or the like beingexecuted by, for example, the processing element 310. Thus, thedatabases, database instances, database management system entities,data, images, applications, programs, program modules, scripts, sourcecode, object code, byte code, compiled code, interpreted code, machinecode, executable instructions, and/or the like may be used to controlcertain aspects of the operation of the computing entity 300 with theassistance of the processing element 310 and operating system.

As will be appreciated, one or more of the computing entity's componentsmay be located remotely from other computing entity components, such asin a distributed system. Furthermore, one or more of the components maybe aggregated and additional components performing functions describedherein may be included in the computing entity 300. Thus, the computingentity 300 can be adapted to accommodate a variety of needs andcircumstances.

Exemplary System Operation

The logical operations described herein may be implemented (1) as asequence of computer implemented acts or one or more program modulesrunning on a computing system and/or (2) as interconnected machine logiccircuits or circuit modules within the computing system. Theimplementation is a matter of choice dependent on the performance andother requirements of the computing system. Accordingly, the logicaloperations described herein are referred to variously as states,operations, structural devices, acts, or modules. These operations,structural devices, acts, and modules may be implemented in software, infirmware, in special purpose digital logic, and any combination thereof.Greater or fewer operations may be performed than shown in the figuresand described herein. These operations may also be performed in adifferent order than those described herein.

Inventory Sorting Module

Turning now to FIG. 4, additional details are provided regarding aprocess flow for sorting an inventory of processing objects according tovarious embodiments. The term “inventory” is used to generally refer toa grouping/listing of processing objects. Here, FIG. 4 is a flow diagramshowing an inventory sorting module for performing such functionalityaccording to various embodiments of the invention. For example, the flowdiagram shown in FIG. 4 may correspond to operations carried out by aprocessing element 310 in a computing entity 300, such as an applicationserver 210 described in FIG. 2, as it executes the inventory sortingmodule stored in the computing entity's volatile and/or nonvolatilememory.

The process flow 400 shown in FIG. 4 begins in various embodiments withthe inventory sorting module selecting a processing object from aninventory in Operation 410. In particular embodiments, the inventorysorting module may be configured to select the processing objects fromthe inventory one at time based on a FIFO based. While in otherembodiments, the inventory sorting module may be configured to selectthe processing objects based on some type of criteria such as, forexample, objects associated with a particular customer may beprioritized over objects for other customers found in the inventory.

Accordingly, the inventory sorting module applies one or moredeterministic prioritization rules in Operation 415. As previouslydiscussed, the deterministic prioritization rules in various embodimentsinvolve rules that have a high degree of reliability in identifyingwhich objects in the inventory are likely to be members of a targetgroup. For instance, returning to the example, the deterministicprioritization rules may be those rules with a past investigatorysuccess measure of identifying claims subject to overpayments over acertainty threshold.

The inventory sorting module determines in Operation 420 as to whetherat least one of the deterministic prioritization rules applies to theprocess object. If none of the rules applies, then the inventory sortingmodule places the processing object in a low priority list in Operation425. If a deterministic prioritization rule does apply (or requiredcombination thereof), then the inventory sorting module places theprocessing object in a high priority list in Operation 430. Here, thetwo priority lists separate the processing objects found in theinventory into those objects with a high likelihood of being members ofa target group (e.g., those insurance claims with a high likelihood ofbeing subject to overpayments) and those objects with a low likelihoodof being members of the target group.

At this point, the inventory sorting module determines whether anotherobject is found in the inventory in Operation 435. If so, then themodule returns to Operation 410 and selects the next processing objectfrom the inventory. The inventory sorting module then repeats theoperations just discussed to place the newly selected processing objecton the low priority list or the high priority list.

Once the inventory sorting module has sorted all of the processingobjects found in the inventory, the module prioritizes the processingobjects found on the low priority list in Operation 440. Here, inparticular embodiments, the inventory sorting module performs thisoperation by invoking a low-priority prioritization module and thelow-priority prioritization module prioritizes the processing objectsfound on the low priority list relative each other with respect to animportance of being investigated. For instance, returning to theexample, the claims found on the low priority list are prioritizedrelative to each other based on their likelihood of being subject tooverpayments.

As previously detailed, in particular embodiments, the processingobjects found on the high priority list may be immediately sent forinvestigation to determine if in fact they are members of the targetgroup. For instance, in the example, the insurance claims found on thehigh priority list may be immediately sent to auditors to determinewhether they are in fact subject to overpayments.

However, in other embodiments, the processing objects found on the highpriority list may first be prioritized before being sent forinvestigation and/or may be further processed before being sent forinvestigation. Therefore, in particular embodiments, the inventorysorting module may be configured to prioritize the processing objectsfound on the high priority list in Operation 445. Again, the moduleperforms this operation in particular embodiments by invoking ahigh-priority prioritization module. In turn, the high-priorityprioritization module prioritizes the objects found on the high prioritylist relative to each other. However, as noted, the inventory sortingmodule may not be configured to perform this operation in particularembodiments. The module may be configured in this fashion because all ofthe processing objects found on the high priority list are likely to beinvestigated within the processing time constraint and therefore, thereis no need to prioritize these objects.

Low-Priority Prioritization Module

Turning now to FIG. 5, additional details are provided regarding aprocess flow for prioritizing a set of low priority processing objectsaccording to various embodiments. Here, FIG. 5 is a flow diagram showinga low-priority prioritization module for performing such functionalityaccording to various embodiments of the invention. For example, the flowdiagram shown in FIG. 5 may correspond to operations carried out by aprocessing element 310 in a computing entity 300, such as an applicationserver 210 described in FIG. 2, as it executes the low-priorityprioritization module stored in the computing entity's volatile and/ornonvolatile memory.

As previously mentioned, the low-priority prioritization module may beinvoked by another module in various embodiments to prioritize a set oflow priority processing objects. For instance, in particularembodiments, the low-priority prioritization module may be invoked bythe inventory sorting module as previously described. However, with thatsaid, the low-priority prioritization module may not necessarily beinvoked by another module and may execute as a stand-alone module inother embodiments.

The process flow 500 begins with the low-priority prioritization moduleselecting a low priority processing object in Operation 510. Aspreviously noted, the goal of prioritizing the low priority processingobjects is to produce a prioritized listing of the objects so that theobjects with a higher likelihood of being a member of the target groupwill be investigated before expiry of the time allocate for theinvestigation process.

Therefore, the low-priority prioritization module is configured invarious embodiments to apply one or more probabilistic prioritizationrules to the processing object in Operation 515. As previouslymentioned, these rules may have a certain degree of reliability inidentifying which objects in the inventory are likely to be members of atarget group. Here, in particular embodiments, the probabilisticprioritization rules may have a past investigatory success measure overa relevance threshold.

Typically, the relevance threshold is less than the certainty thresholdused in identifying the deterministic prioritization rules. Thus, acandidate rule that is selected to serve as a probabilisticprioritization rule may not have a certainty of identifying processingobjects that are members of a target group that is acceptable to serveas a deterministic prioritization rule, but the rule still has a levelof certainty that makes the rule useful as a probabilisticprioritization rule.

In addition, in particular embodiments, a probabilistic weight value isassigned to each probabilistic prioritization rule. This weight valuemay be based on the past investigatory success measure for the rule.Here, in particular embodiments, the low-priority prioritization modulemay be configured to combine (e.g., add or multiple) the probabilisticweight values of the probabilistic prioritization rules that aresatisfied by the processing object to determine a rule-based priorityscore for the object.

The low-priority prioritization module also applies one or more machinelearning models to the processing object in various embodiments toidentify a probability of the object being a member of the target groupin Operation 520. The one or more machine learning models may bedesigned to assign a probability to the object based on the likelihoodof the processing object being a member of the target group. Forexample, in particular embodiments, the models may assign amachine-learning-based probability score between zero and one withrespect to the object being a member of the target group. The closer theprobability is to one, the more likely the object is a member of thetarget group. Examples of machine learning models that can be used togenerate machine-learning-based probability scores include neuralnetworks, support vector machines (SVMs), Bayesian networks,unsupervised machine learning models such as clustering models, and/orthe like.

Once the low-priority prioritization module has applied theprobabilistic prioritization rules and determined a rule-based priorityscore for the object and applied the one or more machine learning modelsto generate a machine-learning-based probability score indicating thelikelihood of the processing object being a member of the target group,the module calculates a hybrid prioritization score in Operation 525.

Here, the low-priority prioritization module may calculate the hybridprioritization score by combining the rule-based priority score and themachine-learning-based probability score. In addition, the low-priorityprioritization module may include a valuation magnitude as a multiple.For instance, in particular embodiments, the low-priority prioritizationmodule calculates the hybrid prioritization score by multiplying therule-based priority score, the machine-learning-based probability score,and the valuation magnitude for the processing object. As previouslynoted, the valuation magnitude is a measure of value. For instance, inthe example, the valuation magnitude for an insurance claim may be thedollar amount for the claim. Accordingly, the hybrid prioritizationscore may represent a more complete prioritization score that results ina more efficient and effective investigation process in variousembodiments.

At this point, the low-priority prioritization module determines whetheranother low priority processing object exists in Operation 530. If so,then the module returns to Operation 510 and selects the next processingobject. The low-priority prioritization module then repeats theoperations just discussed for the newly selected low priority processingobject.

Once all of the low priority processing objects have been processed, thelow-priority prioritization module builds a prioritized list based onthe hybrid prioritization scores for the objects in Operation 535.Accordingly, the prioritized list provides a listing of all of the lowpriority processing objects in order of importance relative to eachother. The importance being a combination of the likelihood of theprocessing object being a member of the target group and the valuationmagnitude of the object. Therefore, in the example, the importance is acombination of the likelihood the low priority insurance claim issubject to an overpayment and the dollar value of the claim.

The produced prioritized list of low priority processing objects canthen be used to conduct the investigation of the processing objectsfound on the list. In general, the processing objects are selected offthe list for investigation in order of importance. This allows for theobjects with higher importance on the list be processed first so thatsuch objects are likely investigated within the time constraint placedon conducting the investigation.

High-Priority Prioritization Module

As previously noted, the processing objects identified as high prioritymay also be prioritized in various embodiments. Therefore, turning nowto FIG. 6, additional details are provided regarding a process flow forprioritizing a set of high priority processing objects according tovarious embodiments. Here, FIG. 6 is a flow diagram showing ahigh-priority prioritization module for performing such functionalityaccording to various embodiments of the invention. For example, the flowdiagram shown in FIG. 6 may correspond to operations carried out by aprocessing element 310 in a computing entity 300, such as an applicationserver 210 described in FIG. 2, as it executes the high-priorityprioritization module stored in the computing entity's volatile and/ornonvolatile memory.

As previously mentioned, the high-priority prioritization module may beinvoked by another module in various embodiments to prioritize a set ofhigh priority processing objects. For instance, in particularembodiments, the high-priority prioritization module may be invoked bythe inventory sorting module as previously described. However, with thatsaid, the high-priority prioritization module may not necessarily beinvoked by another module and may execute as a stand-alone module inother embodiments.

The process flow 600 begins with the high-priority prioritization moduleselecting a high priority processing object in Operation 610. Onceselected, the high-priority prioritization module determines a priorityscore for the object in Operation 615. Depending on the embodiment, thehigh-priority prioritization module may be configured to determine thepriority score for the processing object using a number of differentapproaches.

For instance, in particular embodiments, the high-priorityprioritization module may simply use a valuation magnitude for theprocessing object as the priority score for the object such as, forexample, the dollar amount of an insurance claim. This approach allowsfor the high priority processing objects with the higher value to beprocessed first and thus, can lead to a higher value realization fromthe investigation if for some reason all of the high priority processingobjects cannot be properly investigated within the time constraint.While in other embodiments, each of the deterministic prioritizationrules may also be assigned a weighting similar to the probabilisticprioritization rules. Here, the high-priority prioritization module maydetermine the priority score for a high priority processing object inthe same manner as the low-priority prioritization module calculates therule-based priority score for a low priority processing object. Whileanother approach may be for the high-priority prioritization module todetermine a hybrid prioritization score similar to the low-priorityprioritization module.

The high-priority prioritization module then determines whether anotherhigh priority processing object exists in Operation 620. If so, then themodule returns to Operation 610 and selects the next processing object.The low-priority prioritization module then repeats the operations justdiscussed for the newly selected low priority processing object.

Once all of the high priority processing objects have been processed,the high-priority prioritization module builds a prioritized list basedon the priority scores for the objects in Operation 625. Accordingly,the prioritized list provides a listing of all of the high priorityprocessing objects in order of importance relative to each other. Atthis point, the prioritized list can be used to conduct theinvestigation of the high priority processing objects found on the list.

Object Investigation Module

Turning now to FIG. 7, additional details are provided regarding aprocess flow for investigating one or more prioritized lists ofprocessing objects according to various embodiments. Here, FIG. 7 is aflow diagram showing an object investigation module for performing suchfunctionality according to various embodiments of the invention. Forexample, the flow diagram shown in FIG. 7 may correspond to operationscarried out by a processing element 310 in a computing entity 300, suchas an application server 210 described in FIG. 2, as it executes theobject investigation module stored in the computing entity's volatileand/or nonvolatile memory.

The process flow 700 shown in FIG. 7 begins in various embodiments withthe object investigation module selecting a processing object from thehigh priority list in Operation 710. As previously noted, the processingobjects identified as high priority may or may not be prioritized withrespect to each other depending on the embodiment. In addition, theprocessing objects identified as high priority may simply be sent to aninvestigatory subset without further consideration in particularembodiments. For instance, returning to the example, the insuranceclaims identified as being high priority may simply be placed in aninvestigatory subset for auditors to investigate for overpaymentswithout the claims being further filtered.

However, with that said, the high priority processing objects may befurther filtered through automation in particular embodiments asdemonstrated in the process flow 700 shown in FIG. 7. Here, the processflow 700 is configured to work through the list of high priorityprocessing objects first before moving on to the prioritized list forthe low processing objects. Such a configuration is used in variousembodiments to ensure the high priority processing objects are properlyinvestigated within the time constraint placed on the investigationprocess.

Therefore, the object investigation module performs one or more checkson the selected object in Operation 715. Here, the object investigationmodule performs this operation in particular embodiments by applying oneor more rules to the processing object to identify whether any issuescan be found with respect to the object that would warrant a furtherinvestigation (e.g., inspection or audit) to determine whether or notthe object is actually a member of the target group. That is to say forthe example, whether or not any issues can be found with respect to aninsurance claim that would warrant having an auditor investigate theclaim to determine whether or not the claim is actually subject to anoverpayment.

The set of rules that are applied for the checks may be based on anynumber of different criteria and combination thereof. For instance, oneor more of rules may be based on business criteria such as, for example,a combination of cost and insurance procedure found on a healthinsurance claim. One or more of the rules may be based on missing and/orsuspect information associated with a processing object such as, forexample, a missing vendor for a purchased component used inmanufacturing. While one or more of the rules may be based onnonconformant variables associated with the processing object such as,for example, a nonconformant temperature at which a steel coil was hotrolled from a slab. Those of ordinary skill in the art can envisionother criteria that may be used in defining the rules used in the checksdepending on the environment in which the investigation is beingconducted in light of this disclosure.

Once the object investigation module has checked the processing objectto determine whether an issue exists warranting further investigation,the module determines whether another high priority processing objectexists on the high priority list in Operation 720. If so, then theobject investigation module returns to Operation 710, selects the nexthigh priority processing object from the list, and performs the checkson the newly selected object.

The object investigation module then moves on to checking the lowpriority processing objects during the time remaining once all of thehigh priority processing objects have been checked. Accordingly, theobject investigation module selects the first low priority processingobject (e.g., the object with the highest priority) from the lowpriority list in Operation 725 and performs the one or more checks onthe selected object in Operation 730. Again, the object investigationmodule performs this operation in particular embodiments by invoking theobject investigation module.

Once the object investigation module has checked the low priorityprocessing object, the module determines whether the time constraint hasexpired in Operation 735. If so, then the object investigation module isconfigured to release the remaining low priority processing objects fromthe low priority list in Operation 740. As a result, the deprioritizedprocessing objects are marked as investigated (e.g., inspected oraudited) and returned without further processing in particularembodiments. For instance, returning to the example, the low priorityinsurance claims are returned to the claims examiners as audited withouthaving auditors review them (without any human intervention). Althoughthe deprioritized processing objects have not been completelyinvestigated, these objects often have lower probability of being amember of the target group and/or lower valuation magnitude since theyare lower in the prioritization listing of the low priority objects.

If the time constraint has not expired, then the object investigationmodule determines whether another processing object is present on thelow priority list in Operation 745. If so, then the object investigationmodule returns to Operation 730, selects the next processing object fromthe list, and performs the checks on the newly selected object. Theprocess flow 700 ends once all of the low priority processing objectshave been checked or the time constraint has expired.

Investigating a Selected Processing Object

Turning now to FIG. 8, additional details are provided regarding aprocess flow for investigating a selected processing object according tovarious embodiments. Here, FIG. 8 is a flow diagram showing an objectinvestigation module for performing such functionality according tovarious embodiments of the invention. For example, the flow diagramshown in FIG. 8 may correspond to operations carried out by a processingelement 310 in a computing entity 300, such as an application server 210described in FIG. 2, as it executes the object investigation modulestored in the computing entity's volatile and/or nonvolatile memory.

The process flow 800 shown in FIG. 8 begins in various embodiments withthe object investigation module performing one or more checks on theprocessing object in Operation 810. As previously mentioned, thesechecks may include one or more rules that are applied to the processingobject to determine whether an issue exists with respect to the objectindicating that it is likely a member of the target group (e.g., theinsurance claim is likely subject to an overpayment).

Therefore, the object investigation module determines whether theprocessing object has passed the checks in Operation 815. If not, thenthe object investigation module in particular embodiments places theprocessing object in an investigatory subset in Operation 820. As aresult, a further investigation may be conducted on the processingobject to determine whether it is actually a member of the target group.

For instance, in the example, an insurance claim placed in theinvestigatory subset may be reviewed by an auditor to determine whetherthe claim is actually subject to an overpayment. If the auditordetermines the claim is subject to an overpayment, the claim can then beadjusted accordingly to address the overpayment. Thus, variousembodiments of the invention allow for more claims with a highlikelihood of overpayments to be worked as a priority. Furthermore, inparticular instances, any adjustments needed to be made may be carriedvia an automated process to further minimize the auditor's involvement.

If the object investigation module instead determines the processingobject has passed the checks, then the module in various embodimentsreleases the processing object in Operation 825, allowing it to bypassfurther investigation. For instance, in the example, if the insuranceclaim if found to have no issues, the claim is returned to theoriginating examiner without further review. As a result, the insuranceclaim is returned without having an auditor investigate the claim foroverpayment. That is to say, the insurance claim is returned withouthaving to use any of the auditor's value time. This provides the auditorwith more time to work on other claims that are more likely to besubject to overpayments.

CONCLUSION

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it is tobe understood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation.

1. A computer-implemented method for performing prioritized processingof a plurality of processing objects under a processing time constraint,the computer-implemented method comprising: applying a priority policyto the plurality of processing objects to identify a high prioritysubset of processing objects and a low priority subset of processingobjects, wherein (i) the high priority subset and the low prioritysubset are identified based at least in part on one or moredeterministic prioritization rules, and (ii) a probabilistic ordering ofthe low priority subset is based at least in part on one or moreprobabilistic prioritization rules and a priority determination machinelearning model; until a termination time determined based at least inpart on the processing time constraint and in accordance with theprobabilistic ordering of the low priority subset of processing objects:using a data processing model to determine whether to add a particularprocessing object in the low priority subset of processing objects to aninvestigatory subset, wherein the investigatory subset comprises each ofthe processing objects in the high priority subset of processingobjects; and modifying the investigatory subset to add the particularprocessing object in response to determining to add the particularprocessing object to the investigatory subset; and providing aninvestigatory output user interface for display, wherein theinvestigatory output user interface describes each processing objectfound in the investigatory subset.
 2. The computer-implemented method ofclaim 1, wherein the high priority subset of processing objectscomprises each of the processing objects of the plurality of processingobjects that satisfies at least one of the one or more deterministicprioritization rules and the low priority subset of processing objectscomprises each of the processing objects of the plurality of processingobjects that fails to satisfy any of the one or more deterministicprioritization rules.
 3. The computer-implemented method of claim 1,wherein each of the one or more probabilistic prioritization rules isassociated with a probabilistic weight value, and the method furthercomprises determining a hybrid priority score for each processing objectin the low priority subset of processing objects by: determining arule-based priority score for the processing object based at least inpart on the probabilistic weight value for each probabilisticprioritization rule of the one or more prioritization rules that issatisfied by the processing object; determining a machine-learning-basedpriority score for the processing object by using the prioritydetermination machine learning model; and determining the hybridpriority score for the processing object based at least in part on theprobabilistic weight value and the machine-learning-based priorityscore.
 4. The computer-implemented method of claim 3, wherein each ofthe probabilistic weight values is based at least in part on a pastinvestigatory success measure for the corresponding probabilisticprioritization rule.
 5. The computer-implemented method of claim 3,wherein the hybrid priority score for the processing object is furtherbased at least in part on a valuation magnitude for the processingobject.
 6. The computer-implemented method of claim 1, whereindetermining the one or more deterministic prioritization rules and theone or more probabilistic prioritization rules comprises: identifying apast investigatory success measure for each of a plurality of candidateprioritization rules; determining the one or more deterministicprioritization rules based at least in part on a first subset of theplurality of candidate prioritization rules whose past investigatorysuccess measures exceed a certainty threshold; and determining the oneor more probabilistic prioritization rules based at least in part on asecond subset of the plurality of candidate prioritization rules whosepast investigatory success measures fall between the certainty thresholdand a relevance threshold.
 7. The computer-implemented method of claim1, wherein each of the plurality of processing objects describes anexaminable claim and the data processing model comprises an overpaidclaim detection model.
 8. The computer-implemented method of claim 1,further comprising: for each processing object in the investigatorysubset, causing a secondary review of the processing object to determinewhether to modify an adjustment subset to comprise the processingobject; causing an adjustment of each processing object in theadjustment subset; subsequent to causing the adjustment of eachprocessing object in the adjustment subset, generating an examinationdata object that describes each processing object in the adjustmentsubset; and causing display of an examination user interface thatdescribes the examination data object.
 9. An apparatus for performingprioritized processing of a plurality of processing objects under aprocessing time constraint, the apparatus comprising at least oneprocessor and at least one memory including a computer program code, theat least one memory and the computer program code configured to, withthe at least one processor, cause the apparatus to: apply a prioritypolicy to the plurality of processing objects to identify a highpriority subset of processing objects and a low priority subset ofprocessing objects, wherein (i) the high priority subset of processingobjects and the low priority subset of processing objects are identifiedbased at least in part on one or more deterministic prioritizationrules, and (ii) a probabilistic ordering of the low priority subset ofprocessing objects is based at least in part on one or moreprobabilistic prioritization rules and a priority determination machinelearning model; until a termination time determined based at least inpart on the processing time constraint and in accordance with theprobabilistic ordering of the low priority subset of processing objects:use a data processing model to determine whether to add a particularprocessing object in the low priority subset of processing objects to aninvestigatory subset, wherein the investigatory subset comprises each ofthe processing objects in the high priority subset of processingobjects; and modify the investigatory subset to add the particularprocessing object in response to determining to add the particularprocessing object to the investigatory subset; and provide aninvestigatory output user interface for display, wherein theinvestigatory output user interface describes each processing objectfound in the investigatory subset.
 10. The apparatus of claim 9, whereinthe high priority subset of processing objects comprises each of theprocessing objects of the plurality of processing objects that satisfiesat least one of the one or more deterministic prioritization rules andthe low priority subset of processing objects comprises each of theprocessing objects of the plurality of processing objects that fails tosatisfy any of the one or more deterministic prioritization rules. 11.The apparatus of claim 9, wherein each of the one or more probabilisticprioritization rules is associated with a probabilistic weight value,and the at least one memory and the computer program code are configuredto, with the at least one processor, cause the apparatus to determine ahybrid priority score for each processing object in the low prioritysubset of processing objects by: determining a rule-based priority scorefor the processing object based at least in part on the probabilisticweight value for each probabilistic prioritization rule of the one ormore prioritization rules that is satisfied by the processing object;determining a machine-learning-based priority score for the processingobject by using the priority determination machine learning model; anddetermining the hybrid priority score for the processing object based atleast in part on the probabilistic weight value and themachine-learning-based priority score.
 12. The apparatus of claim 11,wherein each of the probabilistic weight values is based at least inpart on a past investigatory success measure for the correspondingprobabilistic prioritization rule.
 13. The apparatus of claim 11,wherein the hybrid priority score for the processing object is furtherbased at least in part on a valuation magnitude for the processingobject.
 14. The apparatus of claim 9, wherein the at least one memoryand the computer program code are configured to, with the at least oneprocessor, cause the apparatus to determine the one or moredeterministic prioritization rules and the one or more probabilisticprioritization rules by: identifying a past investigatory successmeasure for each of a plurality of candidate prioritization rules;determining the one or more deterministic prioritization rules based atleast in part on a first subset of the plurality of candidateprioritization rules whose past investigatory success measures exceed acertainty threshold; and determining the one or more probabilisticprioritization rules based at least in part on a second subset of theplurality of candidate prioritization rules whose past investigatorysuccess measures fall between the certainty threshold and a relevancethreshold.
 15. The apparatus of claim 9, wherein each of the pluralityof processing objects describes an examinable claim and the dataprocessing model comprises an overpaid claim detection model.
 16. Theapparatus of claim 9, wherein the at least one memory and the computerprogram code are configured to, with the at least one processor, causethe apparatus to: for each processing object in the investigatorysubset, cause a secondary review of the processing object to determinewhether to modify an adjustment subset to comprise the processingobject; cause an adjustment of each processing object in the adjustmentsubset; subsequent to causing the adjustment of each processing objectin the adjustment subset, generate an examination data object thatdescribes each processing object in the adjustment subset; and causedisplay of an examination user interface that describes the examinationdata object.
 17. A non-transitory computer storage medium comprisinginstructions for performing prioritized processing of a plurality ofprocessing objects under a processing time constraint, the instructionsbeing configured to cause one or more processors to at least performoperations configured to: apply a priority policy to the plurality ofprocessing objects to identify a high priority subset of processingobjects and a low priority subset of processing objects, wherein (i) thehigh priority subset of processing objects and the low priority subsetof processing objects are identified based at least in part on one ormore deterministic prioritization rules, and (ii) a probabilisticordering of the low priority subset of processing objects is based atleast in part on one or more probabilistic prioritization rules and apriority determination machine learning model; until a termination timedetermined based at least in part on the processing time constraint andin accordance with the probabilistic ordering of the low priority subsetof processing objects: use a data processing model to determine whetherto add a particular processing object in the low priority subset ofprocessing objects to an investigatory subset, wherein the investigatorysubset comprises each of the processing objects in the high prioritysubset of processing objects; and modify the investigatory subset to addthe particular processing object in response to determining to add theparticular processing object to the investigatory subset; and provide aninvestigatory output user interface for display, wherein theinvestigatory output user interface describes each processing objectfound in the investigatory subset.
 18. The non-transitory computerstorage medium of claim 17, wherein the high priority subset ofprocessing objects comprises each of the processing objects of theplurality of processing objects that satisfies at least one of the oneor more deterministic prioritization rules and the low priority subsetof processing objects comprises each of the processing objects of theplurality of processing objects that fails to satisfy any of the one ormore deterministic prioritization rules.
 19. The non-transitory computerstorage medium of claim 17, wherein each of the one or moreprobabilistic prioritization rules is associated with a probabilisticweight value, and the operations are further configured to determine ahybrid priority score for each processing object in the low prioritysubset of processing objects by: determining a rule-based priority scorefor the processing object based at least in part on the probabilisticweight value for each probabilistic prioritization rule of the one ormore prioritization rules that is satisfied by the processing object;determining a machine-learning-based priority score for the processingobject by using the priority determination machine learning model; anddetermining the hybrid priority score for the processing object based atleast in part on the probabilistic weight value and themachine-learning-based priority score.
 20. The non-transitory computerstorage medium of claim 19, wherein each of the probabilistic weightvalues is based at least in part on a past investigatory success measurefor the corresponding probabilistic prioritization rule.